{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 2, 3, 4, 5]\n",
    "y = [2.3, 3.4, 1.2, 6.6, 7.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa4ec3a0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.eye(4)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3\n",
       "0  1.0  0.0  0.0  0.0\n",
       "1  0.0  1.0  0.0  0.0\n",
       "2  0.0  0.0  1.0  0.0\n",
       "3  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "reg = linear_model.LinearRegression()\n",
    "reg.fit([ [0, 0], [1, 1], [2, 2] ],       [0, 1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 0.5])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xd444e50>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ITcikYlEJejNLAV4Ehrv7zq+Wu3te+Ps2M5sFDAYqDHoRObr8wiImvrmSlz7dQFLrxrx8Wxpn91ATMjmyEw56M0sCZgI3uPvqMsubAHXcPT98+1Lg0RN9PpHa6t2VW7lv1nK27ivktnO68pNLe9G4vs6QlqOL5PTKV4ALgEQzywUeAuoBuPtk4EHgJOC58Dv8X51G2Q6YFV5WF3jZ3d+qgp9BJK7t3H+IR+dm8doXefRq15Tnrj+bQUlqQiaRi+Ssm2uPMn4bcFsFy3OAU46/NJHazd15PX0zD8/JJL+wiB9e1JM7h/agfl21L5Bjo7/7RGLQlr2F3D87g3dWbOOUzi146po0+rRXEzI5Pgp6kRji7ry6aCNPvLGCotJS7ru8L7ec05UEtS+QE6CgF4kR63ccYMLMDD7O2cmZ3Voz8eoUkhObBF2WxAEFvUjASkqdqf9axy/eXkW9OnV48uqBjDmji9oXSNQo6EUCtGpLPvdMX8ay3L1c3Lctj48aSPsWDYMuS+KMgl4kAIeLS3n2vWyeez+bZg3r8ZtrBzEipYP24qVKKOhFqtkXG/dwz/RlrN66n5GnduShEf1p3aR+0GVJHFPQi1STg4eL+eWC1Uz9cB1tmzXkDzelclFfNSGTqqegF6kGH2XvYPzMDDbsOsj1aUmMG96H5g3VhEyqh4JepArtLSjiyXkreHXRRpJPasyrY8/kzG4nBV2W1DIKepEq8nbWVu6fncH2/EP84Lxu/OjiXjSqnxB0WVILKehFomzH/kM8PCeTuemb6dO+GS/cmEpK55ZBlyW1mIJeJErcnde+yOOR1zPZf6iYn1zSi9vP764mZBI4Bb1IFOTtKeC+WRm8t2o7p3ZpydPXpNCrXbOgyxIBFPQiJ6S01Hnpsw089eZKSkqdB67sx/fOTlYTMokpCnqR47RuxwHGzUjns3W7GNLjJJ68KoWkkxoHXZbIN0RyhampwJXANncfUMG4Ac8AlwMHge+5+9Lw2LDwWALwortPjGLtItVm9uebmDR/FXl7CujQoiGnn9yKBVlbqV+3Dk+PTuHbqZ3VvkBiViR79H8CfgdMq2R8ONAz/JUG/B5IM7ME4FngEiAXWGRmc9w960SLFqlOsz/fxISZGRQUlQCQt7eQvPTNDOjUnD/cdAbtmqsJmcS2o54O4O4LgV1HmDISmOYhnwAtzawDMBjIdvccdz8MvBqeK1KjTJq/6uuQL2vX/sMKeakRonHeVydgY5n7ueFllS2vkJmNNbPFZrZ4+/btUShLJDo27SmocPnmvYXVXInI8YlG0Fd0YNKPsLxC7j7F3VPdPbVNmzZRKEvkxBw4VMwjr2dWOt6xZaNqrEbk+EXjrJtcoEuZ+52BPKB+JctFYt4Ha7YzYWYGubsLOKdHIovX76KwuPTr8Ub1Erj7st4BVigSuWjs0c8BbrSQM4G97r4ZWAT0NLOuZlYfGBOeKxKz9h4s4p7py7jhD59RL6EOf/vBWfzltjQmjk6hU8tGGNCpZSOevHogowZVeiRSJKZEcnrlK8AFQKKZ5QIPAfUA3H0yMI/QqZXZhE6vvDk8VmxmdwHzCZ1eOdXdK/87WCRgby3fwgOvLWfXgcPccUF3fnhRTxrWCzUhGzWok4JdaqyjBr27X3uUcQfurGRsHqFfBCIxa3t+qAnZGxmb6duhOVNvOoOBnVsEXZZI1OiTsVJruTszl27i0blZFBwu4e7LejP2vG7US1ATMokvCnqplXJ3H+TeWctZuHo7p5/ciqdGp9CjbdOgyxKpEgp6qVVKS52/fPolT725EgceHtGPG89Kpo6akEkcU9BLrbF2+37Gz0hn0frdnNszkSeuGkiX1mpCJvFPQS9xr6iklBc+yOHX76yhYd06TLomhWtOVxMyqT0U9BLXlm/ay7gZ6WTm7WNY//Y8Oqo/bZupP43ULgp6iUuFRSX85h9reH5hDq0a1+f315/G8IEdgi5LJBAKeok7i9fv4p4Z6eRsP8A1p3fm/iv60rJx/aDLEgmMgl7ixv5DxUx6ayXTPvmSji0aMe2WwZzXSw3yRBT0Ehf+uXo7987MIG9vATedlczdl/WmSQNt3iKgoJcabs/Bwzw2dwUzlubSrU0T/v6Ds0hNbh10WSIxRUEvNda8jM08+Npydh8s4s6h3fmfC//dhExE/k1BLzXOtn2FPPhaJm9lbqF/x+b8+ZbB9O+oJmQilVHQS43h7vx9SS6Pz82isLiUccP68P1zu1JXTchEjkhBLzXCxl0HuXdWBh+s2cEZya2YODqF7m3UhEwkEgp6iWklpc60j9czaf4qDHhsZH+uTztZTchEjkFEQW9mw4BnCF0p6kV3n1hu/G7g+jKP2Rdo4+67zGw9kA+UAMXunhql2iXOZW/L557p6SzdsIfze7XhZ1cNoHMrNSETOVaRXEowAXgWuITQhcAXmdkcd8/6ao67TwImheePAH7s7rvKPMxQd98R1colbhWVlPL8P9fym39k07hBAr/8r1O4alAnNSETOU6R7NEPBrLdPQfAzF4FRgJZlcy/FnglOuVJbZORu5e7py9j5ZZ8rkjpwMMj+tOmWYOgyxKp0SIJ+k7AxjL3c4G0iiaaWWNgGHBXmcUOLDAzB5539ymVrDsWGAuQlJQUQVkSTwqLSvj1O2t44YMcWjepz/M3nM5l/dsHXZZIXIgk6Cv6e9krmTsC+LDcYZsh7p5nZm2Bt81spbsv/MYDhn4BTAFITU2t7PElDn2as5PxMzNYt+MA30ntwr2X96VF43pBlyUSNyIJ+lygS5n7nYG8SuaOodxhG3fPC3/fZmazCB0K+kbQS+2TX1jE02+t4v8++ZLOrRrxl1vTOKdnYtBlicSdSIJ+EdDTzLoCmwiF+XXlJ5lZC+B84LtlljUB6rh7fvj2pcCj0Shcarb3Vm7jvlkZbN5XyC1DuvLTy3rRuL7O9hWpCkf9n+XuxWZ2FzCf0OmVU90908xuD49PDk+9Cljg7gfKrN4OmBU+W6Iu8LK7vxXNH0Bqll0HDvPY3Cxmfb6Jnm2bMv32szn95FZBlyUS18w99g6Hp6am+uLFi4MuQ6LI3XkjYzMPvZbJ3oIi/vuC7tx5YQ8a1FUTMpFoMLMllX1OSX8rS5Xbuq+Q+2cv5+2srQzs1IK/3JZG3w7Ngy5LpNZQ0EuVcXf+tngjj7+xgsPFpUwY3odbz1ETMpHqpqCXKrFh50HGz0zno7U7Gdy1NU+NTqFrYpOgyxKplRT0ElUlpc4fP1zHLxasJqGO8fioAVw3OElNyEQCpKCXqFm9NdSE7IuNe7iwT1seHzWAji0bBV2WSK2noJcTdri4lN+/v5bfvbeGpg3q8syYU/nWKR3VhEwkRijo5YQs27iHcTPSWbklnxGndOThEf04qamakInEEgW9HJeCwyX86p3VvPhBDm2aNeCFG1O5pF+7oMsSkQoo6OWYfbx2JxNmprN+50GuHdyFCZf3pXlDNSETiVUKeonYvsIiJr65kpc/3UBS68a8fFsaZ/dQEzKRWKegl4j8Y8VW7pu1nG35hXz/3K785JLeNKqv9gUiNYGCXo5o5/5DPPJ6FnOW5dG7XTMm33A6p3ZpGXRZInIMFPRSIXdnzrI8Hnk9i/zCIn50cU/++4Ie1K+r9gUiNY2CXr5h894C7p+1nH+s3MYpXVry9OgUerdvFnRZInKcFPTytdJS59VFG3ly3gqKSku5/4q+3DykKwlqXyBSo0X0d7iZDTOzVWaWbWbjKxi/wMz2mtkX4a8HI11XYsP6HQe47sVPuHdWBgM6tWD+j87jtnO7KeRF4sBR9+jNLAF4FriE0PVjF5nZHHfPKjf1A3e/8jjXlYAUl5QyNdyErH5CHSZePZDvnNFF7QtE4kgkh24GA9nungNgZq8CI4FIwvpE1pUqtnLLPsZNT2dZ7l4u7tuWx0cNpH2LhkGXJSJRFknQdwI2lrmfC6RVMO8sM1sG5AE/dffMY1gXMxsLjAVISkqKoCw5XoeKS3j2vbU89142LRrV47fXDuLKlA7aixeJU5EEfUX/+8tfaHYpcLK77zezy4HZQM8I1w0tdJ8CTIHQNWMjqEuOw+cbdjNuRjqrt+5n1KkdeXBEf1o3qR90WSJShSIJ+lygS5n7nQnttX/N3feVuT3PzJ4zs8RI1pXqcfBwMb9YsJqpH66jffOGTP1eKhf2URMykdogkqBfBPQ0s67AJmAMcF3ZCWbWHtjq7m5mgwmdzbMT2HO0daXqfZi9g/Ez09m4q4Dr05IYP7wPzdSETKTWOGrQu3uxmd0FzAcSgKnunmlmt4fHJwPXAHeYWTFQAIxxdwcqXLeKfhYpZ29BEU/OW8GrizaSfFJjXh17Jmd2OynoskSkmlkoj2NLamqqL168OOgyarQFmVu4f/Zyduw/xPfP68aPL+5Fw3pqQiYSr8xsibunVjSmT8bGmR37D/HwnEzmpm+mT/tmvHhTKimdWwZdlogESEEfJ9yd2V9s4pHXszh4qIT/vaQXPzi/u5qQiYiCPh7k7SngvlkZvLdqO4OSQk3IerZTEzIRCVHQ12Clpc5Ln21g4rwVlDo8eGU/bjo7Wf1pROQ/KOhrqJzt+xk/I4PP1u/inB6JPHn1QLq0bhx0WSISgxT0NUxxSSkv/msdv3p7NfXr1uHp0Sl8O7Wz2heISKUU9DVIVt4+7pmxjOWb9nFpv3Y8NmoA7ZqrCZmIHJmCvgY4VFzC797N5vfvr6Vl43o8e91pXD6wvfbiRSQiCvoYt+TLXYybkUH2tv1cfVonHriiH63UhExEjoGCPkYdOFTMpPmr+PPH6+nYohF/uvkMLujdNuiyRKQGUtDHoA/WbGfCzAxydxdw41knc8+wPjRtoH8qETk+So8YsvdgEY+/kcXfl+TSLbEJf/vBWQzu2jroskSkhlPQx4i3lm/hgdeWs+vAYe64oDs/vKinmpCJSFQo6AO2Lb+Qh+dkMi9jC/06NOeP3zuDAZ1aBF2WiMQRBX1A3J0ZSzfx2NwsCopKuPuy3ow9rxv1EtSETESiS0EfgNzdB7l31nIWrt7O6Se34qnRKfRo2zToskQkTkUU9GY2DHiG0FWiXnT3ieXGrwfGhe/uB+5w92XhsfVAPlACFFfWGL82KC11/u+TL3nqrZUAPPKt/txw5snUURMyEalCRw16M0sAngUuIXSx70VmNsfds8pMWwec7+67zWw4MAVIKzM+1N13RLHuGmft9v2Mm57O4i93c27PRJ64Sk3IRKR6RLJHPxjIdvccADN7FRgJfB307v5RmfmfAJ2jWWRNVlRSypSFOTzzjzU0qpfAz799CqNP66T2BSJSbSIJ+k7AxjL3c/nPvfXybgXeLHPfgQVm5sDz7j6lopXMbCwwFiApKSmCsmLf8k17GTcjncy8fVw+sD0Pf6s/bZupCZmIVK9Igr6iXc8KryhuZkMJBf05ZRYPcfc8M2sLvG1mK9194TceMPQLYAqELg4eQV0xq7CohN/8Yw3PL8yhVeP6TP7uaQwb0CHoskSklook6HOBLmXudwbyyk8ysxTgRWC4u+/8arm754W/bzOzWYQOBX0j6OPFovW7GDc9nZwdB/j26Z25/4p+tGhcL+iyRKQWiyToFwE9zawrsAkYA1xXdoKZJQEzgRvcfXWZ5U2AOu6eH759KfBotIqPJfsPFfP0WyuZ9vGXdGrZiGm3DOa8Xm2CLktE5OhB7+7FZnYXMJ/Q6ZVT3T3TzG4Pj08GHgROAp4Lv8n41WmU7YBZ4WV1gZfd/a0q+UkC9M/V27l3ZgZ5ewv43tnJ3H1Zb5qoCZmIxAhzj73D4ampqb548eKgyziqPQcP8+jcLGYu3UT3Nk14anQKqclqQiYi1c/MllT2OSXtdh4Hd+fN5Vt48LXl7DlYxF1De3DXhT3UhExEYpKC/hht21fIA68tZ37mVgZ0as6fbxlM/45qQiYisUtBHyF35+9Lcnl8bhaFxaWMG9aH75/blbpqQiYiMU5BH4GNuw4yYWYG/8reweDk1kwcPZBubdSETERqBgX9EZSUOtM+Xs/Tb62ijsFjI/tzfZqakIlIzaKgr0T2tnzumZ7O0g17OL9XG564eiCdWjYKuiwRkWOmoC+nqKSUye+v5bfvZtO4QQK/+s4pjDpVTchEpOZS0JeRkbuXu6cvY+WWfK5I6cAj3+pPYtMGQZclInJCFPSEmpD96p3VvLAwh8SmDXj+htO5rH/7oMsSEYmKWh/0n+bsZPzMDNbtOMB3Urtw7xV9adFITchEJH7U2qDPLyziqbdW8pdPNtCldSNeui2NIT0Sgy5LRCTqamXQv7dyG/fNymDzvkJuPacr/3tpLxrXr5UvhYjUArUq3XYdOMxjc7OY9fkmerZtyow7zua0pFZBlyUiUqVqRdC7O3PTN/PwnEz2FhTx/y7qyZ1Du9OgrpqQiUj8i/ug37qvkPtmLeedFVtJ6dyCv9yWRt8OzYMuS0Sk2sRt0Ls7f120kZ/NW8Hh4lLuvbwPtwxREzIRqX0iCnozGwY8Q+gKUy+6+8Ry4xYevxw4CHzP3ZdGsm60zP58E5PmryJvTwFtmzWgeaN6rNm2n7SurXlqdArJiU2q4mlFRGLeUYPezBKAZ4FLCF0ofJGZzXH3rDLThgM9w19pwO+BtAjXPWGzP9/EhJkZFBSVALA1/xBb8w/x7dM789ToFDUhE5FaLZLjGIOBbHfPcffDwKvAyHJzRgLTPOQToKWZdYhw3RM2af6qr0O+rI/W7lTIi0itF0nQdwI2lrmfG14WyZxI1gXAzMaa2WIzW7x9+/YIyvq3vD0Fx7RcRKQ2iSToK9olLn9F8crmRLJuaKH7FHdPdffUNm3aRFDWv3WspH1wZctFRGqTSII+F+hS5n5nIC/COZGse8Luvqw3jcpdmLtRvQTuvqx3tJ9KRKTGiSToFwE9zayrmdUHxgBzys2ZA9xoIWcCe919c4TrnrBRgzrxZPjCIAZ0atmIJ68eyKhBFR4lEhGpVY561o27F5vZXcB8QqdITnX3TDO7PTw+GZhH6NTKbEKnV958pHWr4gcZNaiTgl1EpALmXuEh80Clpqb64sWLgy5DRKTGMLMl7p5a0Zg+JioiEucU9CIicU5BLyIS5xT0IiJxLibfjDWz7cCXx7l6IrAjiuVEi+o6Nqrr2KiuYxOPdZ3s7hV+2jQmg/5EmNniyt55DpLqOjaq69iormNT2+rSoRsRkTinoBcRiXPxGPRTgi6gEqrr2KiuY6O6jk2tqivujtGLiMh/isc9ehERKUNBLyIS52pM0JvZMDNbZWbZZja+gnEzs9+Ex9PN7LRI163iuq4P15NuZh+Z2SllxtabWYaZfWFmUe3iFkFdF5jZ3vBzf2FmD0a6bhXXdXeZmpabWYmZtQ6PVeXrNdXMtpnZ8krGg9q+jlZXUNvX0eoKavs6Wl1BbV9dzOw9M1thZplm9sMK5lTdNubuMf9FqMXxWqAbUB9YBvQrN+dy4E1CV7U6E/g00nWruK6zgVbh28O/qit8fz2QGNDrdQEw93jWrcq6ys0fAbxb1a9X+LHPA04DllcyXu3bV4R1Vfv2FWFd1b59RVJXgNtXB+C08O1mwOrqzLCaskcfqxcoP+pju/tH7r47fPcTQlfZqmon8jMH+nqVcy3wSpSe+4jcfSGw6whTgti+jlpXQNtXJK9XZQJ9vcqpzu1rs7svDd/OB1bwzetnV9k2VlOCvlouUF5FdZV1K6Hf2F9xYIGZLTGzsVGq6VjqOsvMlpnZm2bW/xjXrcq6MLPGwDBgRpnFVfV6RSKI7etYVdf2Fanq3r4iFuT2ZWbJwCDg03JDVbaNHfUKUzGiWi5QfhwifmwzG0roP+I5ZRYPcfc8M2sLvG1mK8N7JNVR11JCvTH2m9nlwGygZ4TrVmVdXxkBfOjuZffOqur1ikQQ21fEqnn7ikQQ29exCGT7MrOmhH65/Mjd95UfrmCVqGxjNWWPPlYvUB7RY5tZCvAiMNLdd3613N3zwt+3AbMI/YlWLXW5+z533x++PQ+oZ2aJkaxblXWVMYZyf1ZX4esViSC2r4gEsH0dVUDb17Go9u3LzOoRCvmX3H1mBVOqbhurijceov1F6C+PHKAr/34zon+5OVfwn29kfBbpulVcVxKha+meXW55E6BZmdsfAcOqsa72/PsDc4OBDeHXLtDXKzyvBaHjrE2q4/Uq8xzJVP7mYrVvXxHWVe3bV4R1Vfv2FUldQW1f4Z99GvDrI8ypsm2sRhy68Ri9QHmEdT0InAQ8Z2YAxR7qTtcOmBVeVhd42d3fqsa6rgHuMLNioAAY46GtKujXC+AqYIG7HyizepW9XgBm9gqhM0USzSwXeAioV6auat++Iqyr2revCOuq9u0rwroggO0LGALcAGSY2RfhZfcS+kVd5duYWiCIiMS5mnKMXkREjpOCXkQkzinoRUTinIJeRCTOKehFROKcgl5EJM4p6EVE4tz/B58cSuKXPwjOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([0,1,2], [0,1,2])\n",
    "plt.plot([0,1,2], [0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
